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from transformers import XLNetForSequenceClassification, XLNetTokenizer,BertForSequenceClassification,BertTokenizer, RobertaForSequenceClassification,RobertaTokenizer |
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import torch |
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from typing import Dict |
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import gradio as gr |
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model = BertForSequenceClassification.from_pretrained("./Personality_detection_Classification_Save/", num_labels=5) |
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tokenizer = BertTokenizer.from_pretrained('./Personality_detection_Classification_Save/', do_lower_case=True) |
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model.config.label2id= { |
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"Extroversion": 0, |
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"Neuroticism": 1, |
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"Agreeableness": 2, |
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"Conscientiousness": 3, |
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"Openness": 4, |
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} |
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model.config.id2label={ |
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"0": "Extroversion", |
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"1": "Neuroticism", |
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"2": "Agreeableness", |
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"3": "Conscientiousness", |
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"4": "Openness",} |
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def Personality_Detection_from_reviews_submitted (model_input: str) -> Dict[str, float]: |
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if len(model_input)<20: |
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ret ={ |
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"Extroversion": float(0), |
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"Neuroticism": float(0), |
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"Agreeableness": float(0), |
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"Conscientiousness": float(0), |
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"Openness": float(0),} |
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return ret |
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else: |
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dict_custom={} |
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Preprocess_part1=model_input[:len(model_input)] |
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Preprocess_part2=model_input[len(model_input):] |
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dict1=tokenizer.encode_plus(Preprocess_part1,max_length=1024,padding=True,truncation=True) |
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dict2=tokenizer.encode_plus(Preprocess_part2,max_length=1024,padding=True,truncation=True) |
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dict_custom['input_ids']=[dict1['input_ids'],dict1['input_ids']] |
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dict_custom['token_type_ids']=[dict1['token_type_ids'],dict1['token_type_ids']] |
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dict_custom['attention_mask']=[dict1['attention_mask'],dict1['attention_mask']] |
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outs = model(torch.tensor(dict_custom['input_ids']), token_type_ids=None, attention_mask=torch.tensor(dict_custom['attention_mask'])) |
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b_logit_pred = outs[0] |
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pred_label = torch.sigmoid(b_logit_pred) |
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ret ={ |
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"Extroversion": float(pred_label[0][0]), |
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"Neuroticism": float(pred_label[0][1]), |
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"Agreeableness": float(pred_label[0][2]), |
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"Conscientiousness": float(pred_label[0][3]), |
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"Openness": float(pred_label[0][4]),} |
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return ret |
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model_input = gr.Textbox("Input text here (Note: This model is trained to classify Big Five Personality Traits From Expository text features)", show_label=False) |
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model_output = gr.Label(" Big-Five personality traits Result", num_top_classes=6, show_label=True, label="Big-Five personality traits Labels assigned to this text based on its features") |
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examples = [ |
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( "Well, here we go with the stream-of-consciousness essay. I used to do things like this in high school sometimes.", |
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"They were pretty interesting, but I often find myself with a lack of things to say. ", |
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"I normally consider myself someone who gets straight to the point. I wonder if I should hit enter any time to send this back to the front", |
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"Maybe I'll fix it later. My friend is playing guitar in my room now. Sort of playing anyway.", |
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"More like messing with it. He's still learning. There's a drawing on the wall next to me. " |
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), |
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( "An open keyboard and buttons to push. The thing finally worked and I need not use periods, commas, and all those things.", |
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"Double space after a period. We can't help it. I put spaces between my words and I do my happy little assignment of jibber-jabber.", |
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"Babble babble babble for 20 relaxing minutes and I feel silly and grammatically incorrect. I am linked to an unknown reader.", |
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"A graduate student with an absurd job. I type. I jabber and I think about dinoflagellates. About sunflower crosses and about ", |
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"the fiberglass that has to be added to my lips via clove cigarettes and I think about things that I shouldn't be thinking.", |
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"I know I shouldn't be thinking. or writing let's say/ So I don't. Thoughts don't solidify. They lodge in the back. behind my tongue maybe.", |
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), |
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( "My favorite aspect of debate would actually be --this all gets back to a time when we were assigned to write a bill that we would take to a fake model ", |
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"united nations conference and we would have to present a bill that we wanted to be passed- in fact, my partner and I rarely wanted the bills we proposed to be ", |
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"passed, but we just wanted people to have to argue against them, in most cases we would try to make our bills interesting or at least darkly satirical, ", |
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"so that the only arguments that could be made against them would be based on moral rationalization rather than common reason- the moral debates would most ", |
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"likely get everyone interested and could be defeated by one who was willing not to be moral- none of our bills ever passed-As I write this I find that I am ", |
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"often losing my train of thought but I don't believe that that is how I usually think- as a result of the confines of this experiment I am discovering that", |
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"I am thinking more quickly than I normally do and I can't explain why that is other to keep typing, however, when I am normally thinking, I still try to think", |
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), |
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( "slowly and articulately so as not to speak something that makes me look ignorant-this is said mostly to point to out possible flaws in the ways of tracing thoughts .", |
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"Now in fact I a running out of things to say before I finish, which is still about seven minutes away- I'd like to apologize for the many spelling errors that ", |
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"are sure to be found in his assignment- don't mean the errors that are natural such as words that I just don't know the spelling of but rather, I mean the words ", |
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"that look as if they have been written by an idiot because I am not a very talented typist and my fingers are slipping over the keys, I would go back and fix these ", |
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"errors but that seems contradictory to the nature of the assignment 2:53 was the time at which I am writing this I am also realizing that occasionally there is ", |
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"no clear and concise thought n my head which I can write down or there are just so many thoughts that I can not possibly transfer them onto paper at the rate at which ", |
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"they are passing through- I hate leaving the impression with anyone that I am ignorant and I think that is the main reason I dislike this assignment because I don't ", |
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"see how anyone can read this and not see exactly that- it is my hope that at least everyone will appear ignorant and then at least I will be on even ground.", |
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"I also hate writing this to a professor of psychology because I am sure it is analyzed more than is necessary- if this assignment is done honestly then you could probably ", |
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"just talk to someone and get just as many honest answers- well -I've just hit nineteen minutes and I suppose that last sentence is just as good a place to finish off as any where. ") |
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] |
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title = "<center><a href=\"https://thoucentric.com/\"><img src='https://thoucentric.com/wp-content/themes/cevian-child/assets/img/Thoucentric-Logo.png' alt='Thoucentric-Logo'></a></center><br>Big Five Personality Traits Detection From Expository text features" |
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description = ("<br><br>In traditional machine learning, it can be challenging to train an accurate model if there is a lack of labeled data specific to the task or domain of interest. Transfer learning offers a way to address this issue by utilizing the pre-existing labeled data from a similar task or domain to improve model performance. By transferring knowledge learned from one task to another, transfer learning enables us to overcome the limitations posed by a shortage of labeled data, and to train more effective models even in data-scarce scenarios. We try to store this knowledge gained in solving the source task in the source domain and applying it to our problem of interest. In this work, I have utilized Transfer Learning utilizing BERT BASE UNCASED model to fine-tune on Big-Five Personality traits Dataset.") |
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Fotter = ( |
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"<center>Copyright © 2023 <a href=\"https://thoucentric.com/\">Thoucentric</a>. All Rights Reserved</center>" |
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) |
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app = gr.Interface( |
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Personality_Detection_from_reviews_submitted, |
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inputs=model_input, |
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outputs=model_output, |
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examples=examples, |
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title=title, |
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description=description, |
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article=Fotter, |
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allow_flagging='never', |
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analytics_enabled=False, |
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) |
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app.launch(show_error=False) |
